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2,113 results for “High resolution”
Figure 4 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 4 Frequency distribution of unambiguously optimized synontomorphies during the growth of Tyrannosaurus rex. Growth stages (corresponding to the numbered nodes of the ontogram in Fig. 2) are along the x-axis and the number of changes are along the y-axis. The greatest number of changes are seen in the transition from large juvenile to subadult, or, from growth stage 5–6; the high concentration of change between these growth categories is evidence that T. rex ontogeny is metamorphic (sensu Rose & Reiss, 1993). Full-size DOI: 10.7717/peerj.9192/fig-4
Figure 5 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 5 Comparison of the frequency distributions of phylogenetic and nonphylogenetic synontomorphies in the ontogeny of Tyrannosaurus rex. Growth stage is along the x-axis (corresponding to the numbered nodes of the ontogram in Fig. 2) and number of synontomorphies is along the y-axis. Phylogenetic characters are in solid bars; nonphylogenetic characters are in hollow bars. The frequency distributions of both sets of data follow the same general pattern, aside from the flatter distribution of the phylogenetic synontomorphies relative to the nonphylogenetic synontomorphies and the reversed pattern seen at growth stages 7 and 8. Both types of changes occur throughout the lifespan of T. rex, indicating that ontogeny is not strictly congruent with phylogeny. Full-size DOI: 10.7717/peerj.9192/fig-5
Figure 16 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 16 Bivariate scatterplot showing the relationship between chronological age with maturity among eight specimens of Tyrannosaurus rex. The comparison is limited to specimens that have been histologically aged; growth stages (x-axis) and chronological age (y-axis) have been converted to ranks. See Table 14 for the raw data. Full-size DOI: 10.7717/peerj.9192/fig-16
Figure 3 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 3 Scatterplot showing the noncongruence in Tyrannosaurus rex between the completeness of specimens (i.e., number of characters scored) and the number of synontomorphies at each corresponding node. Per cent completeness (decreasing away from the origin) and the number of synontomorphies supporting the corresponding node (decreasing away from the origin) have been converted to ranks. A Spearman correlation test on these data results in a nonsignificant correlation coefficient; ergo, the number of synontomorphies at an internode is not an artifact of specimen completeness. Full-size DOI: 10.7717/peerj.9192/fig-3
Figure 30 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 30 Comparison of recapitulatory synontomorphies of Tyrannosaurus rex with tyrannosauroid phylogeny. Ten unambiguously optimized synontomorphies are congruent with unambiguously optimized synapomorphies of tyrannosauroid phylogeny, providing limited evidence of recapitulation (see text for details). Numbers to the right correspond to the growth stages in Fig. 2. If recapitulation was present, then the growth stage numbers should increase with progressively exclusive clades; that pattern is not seen here. Full-size DOI: 10.7717/peerj.9192/fig-30
Figure 29 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 29 Reptile Encephalization Quotients (REQs) of Hurlburt, Ridgley & Witmer (2013) mapped onto the growth curve of Tyrannosaurus rex. The REQ is based on a brain mass to endocranial volume ratio of 37% and the parenthetical values following the REQs corresponds to the two different body mass estimates, in metric tonnes, from which the REQs were derived (see Hurlburt, Ridgley & Witmer, 2013 for details). Overall, the REQ of the juvenile greatly exceeds that of adults, and the adults show an increasing ontogenetic progression of REQ values, as first reported by Hurlburt, Ridgley & Witmer (2013). Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-29
Figure 26 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 26 Heat maps of the ontogenetic changes seen in the skull and mandible of Tyrannosaurus rex. Illustrations show per centage of the total number of unambiguously optimized synontomorphies per bone (A) and functional module (B). Darker shades of gray indicate higher proportions of growth change, whereas lighter shades indicate lower proportions of change. The results show that the greatest amount of growth changes are at the lacrimal (A) or along the dorsal skull roof (B). Hatchure indicates empty space; stipple indicates unprepared matrix. Full-size DOI: 10.7717/peerj.9192/fig-26
Figure 25 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 25 The results of Therrien, Henderson & Ruff (2005) compared with the growth curve of Tyrannosaurus rex. Vertical bending strength and relative bending strength (sensu Therrien, Henderson & Ruff, 2005) mapped onto the growth curve of T. rex. Values for juveniles are missing for mid-dentary dorsoventral strength and mid-dentary relative strength. In general, strength increases ontogenetically, a trend that becomes obscured in adulthood. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-25
Figure 1 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 1 Results of the cladistic analysis of 1,850 characters among 44 specimens of Tyrannosaurus rex. (A) Strict consensus of 50 MPTs showing the recovery of three primary growth stages separated by the specimen BMRP 2002.4.1. (B) The single ontogram recovered after the exclusion of wildcard specimens, reducing the number of OTUs to 31. Numbers to the left of the internodes are bootstrap and jackknife values, respectively; numbers to the right are Bremer decay indices. Asterisk indicates the type specimen. Ellipses enclose the regions of polytomies produced by the wildcard specimens, which are listed in the lower right hand corner of the corresponding ellipse. Note that the ellipses are limited to one side or the other relative to BMRP 2002.4.1, which corresponds to the topology of the strict consensus ontogram. Full-size DOI: 10.7717/peerj.9192/fig-1
Figure 2 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 2 Ontogram of Tyrannosaurus rex showing growth stages, synontomorphies, individual variation, individual specimens, and chronological ages. Arrowhead points to the most mature specimen and the direction of the entire ontogenetic axis; that is, the least mature specimen is at the lower left whereas the most mature specimen is at the upper right. Asterisk indicates the type specimen. Individual variation occurs as progressions until young adulthood, where reversals are first seen. The maximum amount of change occurs at growth stages 5 and 6, which corresponds to the transition from a long and low skull and jaws to a deep and stout skull frame; this event, marked by the concentration of an extreme number of changes, is evidence that the ontogeny of T. rex is metamorphic (sensu Rose & Reiss, 1993). Each circle represents a numbered growth stage; these numbers do not correspond to those seen in Fig. 12. The star at growth stage 7 marks the ~3,000 kg threshold that separates T. rex from its closest, but smaller, relatives. Color key: red, small juveniles; orange, large juveniles; yellow, subadults; green, young adults; blue, adults; violet, senescent adults. See text for definition of growth categories. Skulls are to scale; AMNH FARB 5027 is scaled to a premaxilla to quadrate length of 1.3 m. Full-size DOI: 10.7717/peerj.9192/fig-2
TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet
<p>We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.</p> <p>Temporal coverage: April 2018 until March 2021, 16 day time steps</p> <p>Data for other regions can be requested and produced by the authors. Please refer for further information to: </p> <p>Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). <em>Biogeosciences</em>, <em>19</em>(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022</p>
FIGURE 3. High-resolution X in Systematic revision of Afrogecko ansorgii (Boulenger, 1907) (Sauria: Gekkonidae) from western Angola
FIGURE 3. High-resolution X-ray computed tomographies of Bauerius ansorgii (PEM R23911). Views in (A) dorsal and (B) lateral aspects of body. Detailed CT-scan of (C) pelvic and (D) pectoral girdle in dorsal and ventral view, respectively.
FIGURE 2. High-resolution X in Systematic revision of Afrogecko ansorgii (Boulenger, 1907) (Sauria: Gekkonidae) from western Angola
FIGURE 2. High-resolution X-ray computed tomographs of skull of Bauerius ansorgii (PEM R23912). Detailed views in (A) lateral, (B) medial, (C) dorsal and (D) ventral.
E3SM high resolution simulations
<p>This dataset is E3SM simulation output with different hozitional and vertical resolutions. </p>
Comparability of skeletal fibulae surfaces generated by different source scanning (dual-energy CT scan vs. high resolution laser scanning) and 3D geometric morphometric validation
<p><strong>SI_Appendix 1.</strong> Matrix of Cartesian coordinates of analyzed specimens.</p> <p><strong>SI_Appendix 2</strong>. Sample list and acquisition methods. </p>
High-resolution imaging and manipulation of endogenous AMPA receptor surface mobility during synaptic plasticity and learning
<p><span>Data set for the MS</span></p>
The datasets used in the manuscript named "Fidelity of Global Tropical Cyclone Activity in a High-Resolution Reanalysis Dataset CRA40 in Comparison with Multiple Other Reanalysis Datasets"
<p>The datasets after tracking the TC events in five reanalyses: ERA5, JRA55, CFSR, MERRA2, CRA40. </p>
High resolution opacities for H2/He atmospheres
<p>This set of molecular and atomic opacities has been built based on the same opacity sources used for calculating the correlated-k coefficients in the following repositories:</p> <p>10.5281/zenodo.5590997 (11 windows)</p> <p>10.5281/zenodo.5590995 (30 windows)</p> <p>10.5281/zenodo.5590986 (180 windows)</p> <p>10.5281/zenodo.5590989 (196 windows)</p> <p>These opacities can be used to generate high resolution spectra for the atmospheric structures computed with the correlated-k coefficients above.</p> <p>The set includes C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The opacities are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list. Each *.zip file contains one opacity file for each pressure-temperature layer, with the species’ name, the pressure, and the temperature given in the filename. The opacity is given in units of cm^2/molecule. For completeness, each *.zip file also contains a file named wavelengths.txt, listing the wavelengths corresponding to each opacity point, in units of microns. All the opacities are calculated on the same wavelength grid. </p> <p>The references for the line lists used in these opacity calculations can be found in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper. </p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>
Supplementary Material for "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" article.
<p>Data presented in the study "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" </p>
High-Resolution TURBINE fMRI Dataset 1
<p>Isotropic 0.67 mm visual cortex-slab TURBINE raw dataset 1 (in ISMRMRD format) for "Ultra-High Resolution fMRI at 7T using Radial-Cartesian TURBINE sampling" published in Magnetic Resonance in Medicine.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.